Dataset Information

Gene-based Genomewide Association Analysis: A Comparison Study.

ABSTRACT: The study of gene-based genetic associations has gained conceptual popularity recently. Biologic insight into the etiology of a complex disease can be gained by focusing on genes as testing units. Several gene-based methods (e.g., minimum p-value (or maximum test statistic) or entropy-based method) have been developed and have more power than a single nucleotide polymorphism (SNP)-based analysis. The objective of this study is to compare the performance of the entropy-based method with the minimum p-value and single SNP-based analysis and to explore their strengths and weaknesses. Simulation studies show that: 1) all three methods can reasonably control the false-positive rate; 2) the minimum p-value method outperforms the entropy-based and the single SNP-based method when only one disease-related SNP occurs within the gene; 3) the entropy-based method outperforms the other methods when there are more than two disease-related SNPs in the gene; and 4) the entropy-based method is computationally more efficient than the minimum p-value method. Application to a real data set shows that more significant genes were identified by the entropy-based method than by the other two methods.

SUBMITTER: Kang G

PROVIDER: S-EPMC3731815 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Publications

Gene-based Genomewide Association Analysis: A Comparison Study.

Kang Guolian G Jiang Bo B Cui Yuehua Y

Current genomics 20130601 4

The study of gene-based genetic associations has gained conceptual popularity recently. Biologic insight into the etiology of a complex disease can be gained by focusing on genes as testing units. Several gene-based methods (e.g., minimum p-value (or maximum test statistic) or entropy-based method) have been developed and have more power than a single nucleotide polymorphism (SNP)-based analysis. The objective of this study is to compare the performance of the entropy-based method with the minim ...[more]

PMID: 24294105

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Project description:BackgroundSusceptibility to asthma is influenced by genes and environment; implicated genes may indicate pathways for therapeutic intervention. Genetic risk factors may be useful in identifying subtypes of asthma and determining whether intermediate phenotypes, such as elevation of the total serum IgE level, are causally linked to disease.MethodsWe carried out a genomewide association study by genotyping 10,365 persons with physician-diagnosed asthma and 16,110 unaffected persons, all of whom were matched for ancestry. We used random-effects pooled analysis to test for association in the overall study population and in subgroups of subjects with childhood-onset asthma (defined as asthma developing before 16 years of age), later-onset asthma, severe asthma, and occupational asthma.ResultsWe observed associations of genomewide significance between asthma and the following single-nucleotide polymorphisms: rs3771166 on chromosome 2, implicating IL1RL1/IL18R1 (P=3×10(−9)); rs9273349 on chromosome 6, implicating HLA-DQ (P=7×10(−14)); rs1342326 on chromosome 9, flanking IL33 (P=9×10(−10)); rs744910 on chromosome 15 in SMAD3 (P=4×10(−9)); and rs2284033 on chromosome 22 in IL2RB (P=1.1×10(−8)). Association with the ORMDL3/GSDMB locus on chromosome 17q21 was specific to childhood-onset disease (rs2305480, P=6×10(−23)). Only HLA-DR showed a significant genomewide association with the total serum IgE concentration, and loci strongly associated with IgE levels were not associated with asthma.ConclusionsAsthma is genetically heterogeneous. A few common alleles are associated with disease risk at all ages. Implicated genes suggest a role for communication of epithelial damage to the adaptive immune system and activation of airway inflammation. Variants at the ORMDL3/GSDMB locus are associated only with childhood-onset disease. Elevation of total serum IgE levels has a minor role in the development of asthma. (Funded by the European Commission and others.)

Project description:With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.

Project description:We investigated genetic determinants of single-dose simvastatin pharmacokinetics in a prospective study of 170 subjects and a retrospective cohort of 59 healthy volunteers. In a microarray-based genomewide association study with the prospective data, the SLCO1B1 c.521T>C (p.Val174Ala, rs4149056) single nucleotide variation showed the strongest, genomewide significant association with the area under the plasma simvastatin acid concentration-time curve (AUC; P = 6.0 × 10-10 ). Meta-analysis with the retrospective cohort strengthened the association (P = 1.6 × 10-17 ). In a stepwise linear regression candidate gene analysis among all 229 participants, SLCO1B1 c.521T>C (P = 1.9 × 10-13 ) and CYP3A4 c.664T>C (p.Ser222Pro, rs55785340, CYP3A4*2, P = 0.023) were associated with increased simvastatin acid AUC. Moreover, the SLCO1B1 c.463C>A (p.Pro155Thr, rs11045819, P = 7.2 × 10-6 ) and c.1929A>C (p.Leu643Phe, rs34671512, P = 5.3 × 10-4 ) variants associated with decreased simvastatin acid AUC. Based on these results and the literature, we classified the volunteers into genotype-predicted OATP1B1 and CYP3A4 phenotype groups. Compared with the normal OATP1B1 function group, simvastatin acid AUC was 273% larger in the poor (90% confidence interval (CI), 137%, 488%; P = 3.1 × 10-6 ), 40% larger in the decreased (90% CI, 8%, 83%; P = 0.036), and 67% smaller in the highly increased function group (90% CI, 46%, 80%; P = 2.4 × 10-4 ). Intermediate CYP3A4 metabolizers (i.e., heterozygous carriers of either CYP3A4*2 or CYP3A4*22 (rs35599367)), had 87% (90% CI, 39%, 152%, P = 6.4 × 10-4 ) larger simvastatin acid AUC than normal metabolizers. These data suggest that in addition to no function SLCO1B1 variants, increased function SLCO1B1 variants and reduced function CYP3A4 variants may affect the pharmacokinetics, efficacy, and safety of simvastatin. Care is warranted if simvastatin is prescribed to patients carrying decreased function SLCO1B1 or CYP3A4 alleles.

Project description:BACKGROUND:Modern genotyping platforms permit a systematic search for inherited components of complex diseases. We performed a joint analysis of two genomewide association studies of coronary artery disease. METHODS:We first identified chromosomal loci that were strongly associated with coronary artery disease in the Wellcome Trust Case Control Consortium (WTCCC) study (which involved 1926 case subjects with coronary artery disease and 2938 controls) and looked for replication in the German MI [Myocardial Infarction] Family Study (which involved 875 case subjects with myocardial infarction and 1644 controls). Data on other single-nucleotide polymorphisms (SNPs) that were significantly associated with coronary artery disease in either study (P<0.001) were then combined to identify additional loci with a high probability of true association. Genotyping in both studies was performed with the use of the GeneChip Human Mapping 500K Array Set (Affymetrix). RESULTS:Of thousands of chromosomal loci studied, the same locus had the strongest association with coronary artery disease in both the WTCCC and the German studies: chromosome 9p21.3 (SNP, rs1333049) (P=1.80x10(-14) and P=3.40x10(-6), respectively). Overall, the WTCCC study revealed nine loci that were strongly associated with coronary artery disease (P<1.2x10(-5) and less than a 50% chance of being falsely positive). In addition to chromosome 9p21.3, two of these loci were successfully replicated (adjusted P<0.05) in the German study: chromosome 6q25.1 (rs6922269) and chromosome 2q36.3 (rs2943634). The combined analysis of the two studies identified four additional loci significantly associated with coronary artery disease (P<1.3x10(-6)) and a high probability (>80%) of a true association: chromosomes 1p13.3 (rs599839), 1q41 (rs17465637), 10q11.21 (rs501120), and 15q22.33 (rs17228212). CONCLUSIONS:We identified several genetic loci that, individually and in aggregate, substantially affect the risk of development of coronary artery disease.

Dataset Information

Gene-based Genomewide Association Analysis: A Comparison Study.

Publications

Gene-based Genomewide Association Analysis: A Comparison Study.

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